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In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event.
Pathophysiologic targets in the early phase of acute heart failure syndromes
Mihai Gheorghiade, Leonardo De Luca, Gregg C. Fonarow, Gerasimos Filippatos, Marco Metra, and Gary S. Francis · 2005
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Rehospitalizations among patients in the medicare fee-for-service program
Stephen F Jencks, Mark V Williams, and Eric A Coleman · 2009
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Finding a ”kneedle” in a haystack: Detecting knee points in system behavior
Ville Satopaa, Jeannie Albrecht, David Irwin, and Barath Raghavan · 2011
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Risk prediction in patients with heart failure: a systematic review and analysis
Kazem Rahimi, Derrick Bennett, Nathalie Conrad, Timothy M Williams, Joyee Basu, Jeremy Dwight, Mark Woodward, Anushka Patel, John McMurray, and Stephen MacMahon · 2014
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Feed-forward networks with attention can solve some long-term memory problems
Colin Raffel and Daniel PW Ellis · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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An introduction to survival statistics: Kaplan-Meier analysis
William N Dudley, Rita Wickham, and Nicholas Coombs · 2016
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Nonlinear ICA of Temporally Dependent Stationary Sources
Aapo Hyvarinen and Hiroshi Morioka · 2017
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UMAP: Uniform Manifold Approximation and Projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, et al · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unsupervised scalable representation learning for multivariate time series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi · 2019
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Readmission rates and their impact on hospital financial performance: a study of washington hospitals
Soumya Upadhyay, Amber L Stephenson, and Dean G Smith · 2019
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Subject-aware contrastive learning for biosignals
Joseph Y Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, and Erdrin Azemi · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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A system for massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-Tzur, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2020
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Learning from irregularly-sampled time series: A missing data perspective
Steven Cheng-Xian Li and Benjamin Marlin · 2020
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A survey on principles, models and methods for learning from irregularly sampled time series
Satya Narayan Shukla and Benjamin M Marlin · 2020
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Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann · 2020
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Predicting intensive care unit length of stay and mortality using patient vital signs: Machine learning model development and validation
Khalid Alghatani, Nariman Ammar, Abdelmounaam Rezgui, and Arash Shaban-Nejad · 2021
TCLR: Temporal contrastive learning for video representation
Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah · 2022
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Beyond medical imaging-a review of multimodal deep learning in radiology
Lars Heiliger, Anjany Sekuboyina, Bjoern Menze, Jan Egger, and Jens Kleesiek · 2022
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Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, and Kazem Rahimi · 2022
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Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y Zou · 2022
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Lead-agnostic self-supervised learning for local and global representations of electrocardiogram
Jungwoo Oh, Hyunseung Chung, Joon-myoung Kwon, Dong-gyun Hong, and Edward Choi · 2022
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Cited alongside, same era.
Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model
Son Q Duong, Le Zheng, Minjie Xia, Bo Jin, Modi Liu, Zhen Li, Shiying Hao, Shaun T Alfreds, Karl G Sylvester, Eric Widen, et al · 2021
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Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
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3kg: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations
Bryan Gopal, Ryan Han, Gautham Raghupathi, Andrew Ng, Geoff Tison, and Pranav Rajpurkar · 2021
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Clocs: Contrastive learning of cardiac signals across space, time, and patients
Dani Kiyasseh, Tingting Zhu, and David A Clifton · 2021
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A comprehensive EHR timeseries pre-training benchmark
Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, and Marzyeh Ghassemi · 2021
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Clinical risk prediction with temporal probabilistic asymmetric multi-task learning
A Tuan Nguyen, Hyewon Jeong, Eunho Yang, and Sung Ju Hwang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
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Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series
Sindhu Tipirneni and Chandan K Reddy · 2022
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Contrastive learning of medical visual representations from paired images and text
Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz · 2022
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Deep metric learning for the hemodynamics inference with electrocardiogram signals
Hyewon Jeong, Collin M Stultz, and Marzyeh Ghassemi · 2023
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MIMIC-IV, a freely accessible electronic health record dataset
Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, et al · 2023
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Multimodal pretraining of medical time series and notes
Ryan King, Tianbao Yang, and Bobak J. Mortazavi · 2023
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DuETT: Dual event time transformer for electronic health records
Alex Labach, Aslesha Pokhrel, Xiao Shi Huang, Saba Zuberi, Seung Eun Yi, Maksims Volkovs, Tomi Poutanen, and Rahul G Krishnan · 2023
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Learning missing modal electronic health records with unified multi-modal data embedding and modality-aware attention
Kwanhyung Lee, Soojeong Lee, Sangchul Hahn, Heejung Hyun, Edward Choi, Byungeun Ahn, and Joohyung Lee · 2023
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Sequential multi-dimensional self-supervised learning for clinical time series
Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, and Collin Stultz · 2023
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Early prediction of long hospital stay for intensive care units readmission patients using medication information
Min Zhang and Tsung-Ting Kuo · 2024
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